ML Coding Interview Preparation

Hands-on implementation questions for MLE and SDE-ML interviews

---

Overview

This section covers coding-focused ML interview questions where you'll implement algorithms from scratch, write data processing pipelines, and solve practical ML problems in code.

---

Document Structure

Document	Focus	Key Topics
Coding Overview	How to approach ML coding interviews	Strategy, patterns, common pitfalls
KNN Implementation	K-Nearest Neighbors from scratch	Distance metrics, efficient search
K-Means Implementation	K-Means Clustering	Initialization, convergence, optimal K
CNN Filter Implementation	2D Convolutional Filter	Convolution, padding, stride
Practical Problems	Real-world ML problems	User deletion, toxic text, data splitting
Coding Reference	Quick reference	Code templates, complexity analysis

---

Related Documents

Topic	Location
System Design Framework	ML System Design
ML Fundamentals	ML Fundamentals

---

Study Order

1. Start with Coding Overview for strategy
2. Practice implementations (KNN, K-Means, CNN) until you can write them without reference
3. Work through practical problems
4. Use reference guide for quick review